Researcher perspectives on challenges and opportunities in conservation physiology revealed from an online survey
Bibliographic record
Abstract
Conservation physiology represents a recently emerging arm of conservation science that applies physiological tools and techniques to understand and solve conservation issues. While a multi-disciplinary toolbox can only help to address the global biodiversity crisis, any field can face challenges while becoming established, particularly highly applied disciplines that require multi-stakeholder involvement. Gaining first-hand knowledge of the challenges that conservation physiologists are facing can help characterize the current state of the field and build a better foundation for determining how it can grow. Through an online survey of 468 scientists working at the intersection of physiology and conservation, we aimed to identify characteristics of those engaging in conservation physiology research (e.g. demographics, primary taxa of study), gauge conservation physiology's role in contributing to on-the-ground conservation action, identify the perceived barriers to achieving success and determine how difficult any identified barriers are to overcome. Despite all participants having experience combining physiology and conservation, only one-third considered themselves to be 'conservation physiologists'. Moreover, there was a general perception that conservation physiology does not yet regularly lead to tangible conservation success. Respondents identified the recent conceptualization of the field and the broader issue of adequately translating science into management action as the primary reasons for these deficits. Other significant barriers that respondents have faced when integrating physiology and conservation science included a lack of funding, logistical constraints (e.g. sample sizes, obtaining permits) and a lack of physiological baseline data (i.e. reference ranges of a physiological metric's 'normal' or pre-environmental change levels). We identified 12 actions based on suggestions of survey participants that we anticipate will help deconstruct the barriers and continue to develop a narrative of physiology that is relevant to conservation science, policy and practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".